Preliminary estimation of temporal and spatiotemporal dynamic measures of COVID-19 transmission in Thailand

Abstract

Background As a new emerging infectious disease pandemic, there is an urgent need to understand the dynamics of COVID-19 in each country to inform planning of emergency measures to contain its spread. It is essential that appropriate disease control activities are planned and implemented in a timely manner. Thailand was one of the first countries outside China to be affected with subsequent importation and domestic spread in most provinces in the country. Method A key ingredient to guide planning and implementation of public health measures is a metric of transmissibility which represents the infectiousness of a disease. Ongoing policies can utilize this information to plan appropriately with updated estimates of disease transmissibility. Therefore we present descriptive analyses and preliminary statistical estimation of reproduction numbers over time and space to facilitate disease control activities in Thailand. Results The estimated basic reproduction number for COVID-19 during the study ranged from 2.23-5.90, with a mean of 3.75. We also tracked disease dynamics over time using temporal and spatiotemporal reproduction numbers. The results suggest that the outbreak was under control since the middle of April. After the boxing stadium and entertainment venues, the numbers of new cases had increased and spread across the country. Discussion Although various scenarios about assumptions were explored in this study, the real situation was difficult to determine given the limited data. More thorough mathematical modelling

Evaluating the Impact of Intervention Strategies on the First Wave and Predicting the Second Wave of COVID-19 in Thailand: A Mathematical Modeling Study

“…The model simply assumed static values combined with reporting rates that varied by disease severity only. Even the case data from early in the first wave of the epidemic was limited, which might affect the estimation and prediction of parameter estimates, although the Rt and second-wave prediction results here are in agreement with other modeling studies conducted in Thailand [ 12 , 13 , 45 ] and other countries [ 16 , 43 ]. The compartmental model we developed assumed behavioral homogeneity among the entire population.…”

“…Regarding the model’s projections, the Rt was estimated to be 0.73 (95% CrI: 0.53–0.93) during April and May, indicating the policies implemented were effective and were mitigating the disease burden in Thailand. The reduction of Rt to less than 1 after Thailand implemented strict intervention strategies has also been reported elsewhere [ 12 , 13 ]. Our SEIR model was able to accurately depict COVID-19 cases in Thailand, stratified by age.…”

“…The R0 was initially predicted to be 2.68 (95% credible interval (CrI): 2.47–2.86), and the epidemic doubling time was 6.4 days (95% CrI: 5.8–7.1 days). The estimated R0 for COVID-19 in Thailand was estimated to be 3.75 (95% CI: 2.23–5.90) [ 12 ], while the effective reproduction number (Rt), equivalent to a time-varying R0 dependent of the number of susceptible individuals, was reduced to less than 1 in the period after the announcement of the emergency decree [ 12 , 13 ]. An SEIR model was applied to examine the impact of social distancing and working from home.…”

A foci cohort analysis to monitor successful and persistent foci under Thailand’s Malaria Elimination Strategy

“…Under guidance of the MOPH’s DDC, Thai policymakers swiftly responded to the epidemic in early 2020 and took action, including communicating risks, restricting movement and gatherings, conducting strong surveillance and contact tracing, and closing internal borders [ 35 , 36 ]. The public also took corresponding action—such as leaving Bangkok, which reported a high number of COVID-19 cases but does not have indigenous malaria transmission—for other provinces with higher malaria burden [ 37 , 38 ]. This population redistribution, coupled with potential behavioral changes, may change the foci map in 2020 and beyond.…”

Spatio-temporal estimation of basic and effective reproduction number of COVID-19 and post-lockdown transmissibility in Bangladesh

“…The R 0 will not be constant over time because actions will be taken by policymakers (i.e., lockdown and socio-behavioral factors). The time-varying variation of an epidemic can be estimated by the effective reproduction number over time, R t [ 11 – 13 ]. To estimate R t , we utilized EpiEstim [ 12 ] in R software environment.…”